Internet of Things cooperative positioning experiment system and method for college teaching

Abstract

The invention discloses an Internet of Things cooperative positioning experiment system and method for college teaching, and the system comprises a hardware module and a software module, and has the functions of test verification, data collection and fusion, time synchronization, ad hoc network communication and interface configuration. The experimental system comprises an inertial measurement unit (IMU), a global navigation satellite system (GNSS), a V2X and other sensor units, and comprises the following steps: initializing a new node and configuring a sensor interface, realizing high-precision time synchronization through a time synchronization and compensation module, collecting GNSS, IMU and V2X data, realizing cooperative positioning through a fusion positioning algorithm, and carrying out real-time positioning on the GNSS, IMU and V2X data. The result and the original data are stored or exported to an upper computer for analysis, and a visual experiment result is generated; and a multifunctional platform for an Internet of Things cooperative positioning experiment in a college teaching environment is realized, and the experiment teaching effect and the practical ability of students can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of experimental teaching, and particularly relates to an Internet of Things collaborative positioning experiment method and system for use in college teaching. Background Art

[0002] Currently, in the college teaching environment, the research and experimental teaching on positioning technologies mostly use experimental equipment based on single positioning technologies, such as single-point positioning experimental equipment based on GNSS or IMU. In addition, there is no effective way to conduct deeper and more complex experiments on the Internet of Things in Internet of Things teaching experiments. The traditional experimental platform lacks test tools for high-precision positioning and multi-source sensor collaboration, and it is difficult to meet the Internet of Things teaching requirements for deeper multi-node and multi-sensing collaborative positioning, that is, there is a problem of insufficient test verification functions. In addition, sensor collaborative positioning requires precise time synchronization. Different devices have differences in hardware architecture, communication protocols, data acquisition frequencies, etc., resulting in difficulties in unifying timestamps. And existing experimental equipment usually only provides simple positioning result output, lacking the function of storing and analyzing original data, and cannot meet the in-depth research requirements such as positioning error, trend analysis, and model verification in experiments. Moreover, the interface configuration of existing experimental platforms is fixed and has poor scalability, and cannot meet the requirements of multi-source data fusion and sensor expansion. Summary of the Invention

[0003] In order to overcome the above problems in college Internet of Things experimental education, the present invention provides an Internet of Things collaborative positioning experiment method and system for use in college teaching, aiming to develop a college Internet of Things collaborative positioning experimental system integrating test verification, data acquisition, time synchronization, self-organizing network management, and interface configuration. The system will support collaborative positioning experiments in teaching and scientific research through multi-sensor data acquisition and fusion, advanced time synchronization algorithms, data storage and upload mechanisms, and extensible interface management designs.

[0004] The present invention provides an Internet of Things collaborative positioning experiment method for college experimental education, which includes a hardware module and a software module, and has functions of test verification, data acquisition and fusion, time synchronization, self-organizing network communication, and interface configuration. The devices used include: sensor units such as inertial measurement unit IMU, global navigation satellite system GNSS, V2X, etc. Among them, GNSS is used to obtain satellite positioning information, IMU is used to obtain its own motion state, and V2X is used to implement the collaborative positioning function; Specifically, it includes the following steps: S1. Initialize a new node, configure the sensor interface, and configure the node ID through the self-organizing network communication module to realize the local area network connection of the new node; S2. The newly initialized node obtains the synchronization signal and the standard global time through the time synchronization and compensation module. It determines whether to perform time synchronization by checking whether the synchronization signal P is triggered. If triggered, it corrects the local time of the new node through the constructed clock model to synchronize the local time to the global standard time axis, achieving high-precision time synchronization; if not triggered, it proceeds to the next step. S3. The data acquisition and processing module obtains the data of GNSS, IMU, and surrounding nodes (V2X) required for cooperative positioning. The time synchronization and compensation module synchronizes the data with different timestamps from different sensors to the same time. Based on the fusion positioning algorithm, it realizes the cooperative positioning based on the Internet of Things, and locally stores the results and the original data. It determines whether to export the data. If so, it proceeds to the next step; otherwise, it returns to S2 for looping.

[0005] S4. Export the experimental results and the original data to the host computer through the relevant interface. By performing differential processing on the original data and the reference value, the experimental reference true value is obtained. Analyze, compare, generate visual data, and record the experimental results in the experimental session, thus completing a complete set of experiments and data analysis on the Internet of Things cooperative positioning.

[0006] Further, the specific steps of S2 include: S21. The time synchronization and compensation module sends the synchronization signal P to the node controller through the signal line at fixed time intervals and simultaneously sends the current global standard time to the node controller through the data line to the node controller.

[0007] S22. After receiving the synchronization signal P, the node controller immediately triggers an external interrupt and immediately obtains the local time at the current moment within the interrupt program Because theoretically it is impossible to obtain in real time and there is a decoding delay , that is, for the standard global time corresponding to the local time , the processor can only calculate it at moment. If is used to synchronize and update it will be very complicated, so the synchronization signal and the standard time of the previous synchronization signal are used for synchronization.

[0008] S23. Determine whether it is continuous with the previous signal. By taking the difference between the local time read in the previous step and the local time saved when the previous synchronization signal was read, that is: ; where It is default set to 0 during initialization, representing the local time when the previous synchronization signal P was triggered. is the local time when the current synchronization signal P is triggered. If the difference between its time difference and a fixed time interval is within the error range, the signal is considered continuous and the next step is entered; as shown in the following formula: ; Where is the predefined tolerance error range, represents the fixed time interval of the synchronization signal P, which is set according to the system accuracy. If the condition is not met, it is considered that the previous signal is lost or this signal is the moment when the synchronization signal P is first captured after initialization. For both cases, the next time synchronization step S24 is skipped and S25 is directly performed; S24. According to the judgment of the previous step S23, if the synchronization signal is continuous, this time synchronization step is performed according to the formula: ; Where is the local time maintained by the node controller in real time, is the interrupt delay of the node controller, which is set according to the hardware characteristics and interrupt mechanism of the node controller. Then the next step is entered to read and decode to obtain .

[0009] S25. Read and decode the time at the moment of the synchronization signal P , and update and ; ; ; At this point, the node time synchronization is completed.

[0010] Furthermore, the step S3 specifically includes: S31. Data acquisition, real-time acquisition of data from GNSS, IMU, and V2X sensors through the data acquisition and positioning module. GNSS provides high-precision global positioning information, IMU obtains the acceleration and angular velocity information of the node, and V2X obtains the data required for cooperative positioning from surrounding nodes, including relative distance, speed, and other state parameters.

[0011] S32. Unify the data of the above different sensors to the same standard time axis through the time synchronization and compensation module. Use the time synchronization mechanism to compensate and align the acquisition time of the sensor data to ensure the temporal consistency of the input data of the fusion algorithm.

[0012] S33. Perform anomaly detection on the observed data. If all are normal, the next step is entered; otherwise, re-acquisition is performed.

[0013] S34. Based on the multi-source data after time synchronization, use the Kalman filter algorithm, particle filter algorithm or other applicable fusion algorithms to perform positioning calculation, and obtain the real-time position and motion state of the node.

[0014] S35. Locally store the final positioning result and the original sensor data for subsequent analysis and verification.

[0015] S36. Determine whether the data is exported from local storage to the upper computer or the cloud. If so, jump to step S4 for export; otherwise, jump back to the first loop of S2.

[0016] Furthermore, an Internet of Things collaborative positioning experimental system for college teaching, characterized by including: a self-organizing network communication module, a data acquisition module, a time synchronization and compensation module, a data processing and calculation module, a data recording module, an interface configuration and management module, and a network connection control module. Among them: The self-organizing network communication module is used to support the node to obtain an ID from the self-organizing network terminal and add it to the self-organizing network.

[0017] The data acquisition module is used to collect data from sensors such as V2X, IMU, and GNSS; The time synchronization and compensation module is used for synchronizing the local time of multiple nodes with the global standard time to achieve the unification of distributed nodes; The data processing and calculation module is used for data processing to achieve collaborative positioning calculation, etc.; The interface configuration and management module is used to configure the sensor interface and a college teaching use platform with expandable functions; The data recording module is used for data storage and operation log preservation; The network connection control module is used to communicate with a remote server or cloud platform through wired or wireless means to achieve data transmission and remote monitoring.

[0018] Furthermore, the interface configuration and management module of an Internet of Things collaborative positioning experimental system for college teaching adopts configurable software and hardware interfaces, supports multiple communication protocols such as SPI, I2C, UART, CAN, and USB, and has expandability and configurability. It can achieve fast data transmission and interaction, upload the result data and original data to the PC side, support the batch export and analysis functions of experimental data, and perform trend analysis, error evaluation, and model verification on the exported experimental data through built-in analysis tools, which is convenient for teaching experiment evaluation and improvement.

[0019] Furthermore, the network connection control module of an Internet of Things collaborative positioning experimental system for college teaching supports remote configuration management functions, allowing system parameters and algorithm configurations to be adjusted through a remote server to adapt to different experimental requirements and environmental conditions.

[0020] Compared with the prior art, the present invention has the following advantages: 1) For the situation of misalignment of time axes of distributed Internet of Things nodes and multi-source sensors, based on the time synchronization and compensation module, the present invention can achieve high-precision time synchronization at the microsecond level through methods such as synchronization signals, ensuring the time consistency of multi-sensor data and improving the positioning accuracy.

[0021] 2) Integrating GNSS, IMU, and V2X sensors, through data acquisition and multi-source information collaboration, using fusion positioning algorithms such as Kalman filtering to provide high-precision positioning results in complex environments.

[0022] 3) The present invention adopts a modular design with flexible interfaces, supports multiple communication protocols (SPI, I2C, UART, CAN, USB, etc.), has flexible hardware interface configuration and function expansion capabilities, can dynamically adjust the sensor type and connection method according to experimental requirements, and can upload local data through external interfaces or network connection modules.

[0023] 4) The experimental functions of the present invention are comprehensive. The system supports complete functional processes such as data acquisition, real-time positioning, and visualization of experimental results; experimental data supports batch export and error analysis, facilitating the generation of experimental reports and trend analysis charts. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the hardware module structure of an Internet of Things collaborative positioning experimental system for college teaching described in Embodiment 1 of the present invention; Figure 2 It is a flowchart of an Internet of Things collaborative positioning experimental method for college teaching described in Embodiment 1 of the present invention; Figure 3 It is a flowchart of time synchronization described in Embodiment 1 of the present invention; Figure 4 It is a flowchart of multi-sensor fusion positioning described in Embodiment 1 of the present invention; Figure 5 It is a schematic diagram of the software module structure of an Internet of Things collaborative positioning experimental system for college teaching provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0025] To make the technical solution of the present invention clearer, the following will specifically describe the technical solution provided by the present invention in detail with reference to specific embodiments, and further elaborate on the present invention in conjunction with the accompanying drawings. It can be understood that the specific embodiments described herein are only partial embodiments of the present invention, used to explain the present invention, rather than limiting the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0026] Embodiment 1: As Figure 1 is a schematic structural diagram of a hardware device of an Internet of Things collaborative positioning experimental system for college teaching according to Embodiment 1 of the present invention, including a GNSS receiver unit 1, an IMU unit 2, a V2X unit 3, a host computer 4, a WIFI unit 5, a storage card 6, and a node controller (controller); the GNSS receiver unit, the IMU unit, the V2X unit, the WIFI module, and the storage card are all connected to the controller, and the host computer is connected to the controller through a serial port or the WIFI unit.

[0027] Among them: The GNSS receiver unit is used to receive satellite positioning signals, GPS time, and second pulse signals for time synchronization; The IMU is used to obtain its own motion state and attitude information, and jointly use GNSS and V2X information to achieve collaborative positioning; The V2X unit is used to obtain roadside node and surrounding moving node information, and establish a local area network connection with surrounding nodes to obtain the relative distance from surrounding nodes and achieve collaborative positioning; The WIFI unit is used to interact with collaborative positioning nodes through a wireless network to achieve the upload of relevant data or convenient function configuration.

[0028] The storage card is used to save positioning data, sensor raw data, and relevant log information, such as node error reporting and other information.

[0029] The host computer is used to analyze the data stored in the Internet of Things collaborative positioning nodes, calculate the positioning coordinate trajectory that can be used as the experimental true value through software, and conduct experimental analyses such as error analysis; The controller is used to receive and process the measurement data of the GNSS receiver unit, the IMU unit, and the V2X unit.

[0030] As Figure 2It is a flowchart of an Internet of Things collaborative positioning experiment method for college teaching in Embodiment 1 of the present invention. This embodiment is applicable to the Internet of Things collaborative positioning experiment in colleges and universities. Different from the usually relatively simple indoor Internet of Things experiments, this method uses multi-sensor collaboration, addresses the problem of time asynchronization among multi-sensors and multi-nodes, fuses the information obtained by multiple sensors in a complex environment, stores its results and original data simultaneously, and cooperates with the host computer software to achieve data analysis and experiments.

[0031] Specifically, it includes the following steps: Step 1: Node initialization. Configure the sensor interfaces of the experimental nodes, allocate a second pulse external trigger interrupt channel for the GNSS receiver on the node controller; allocate an Ethernet communication channel for the V2X sensor to connect it to the node controller; allocate communication channels for the above several units, as well as the IMU and memory card, and allocate appropriate buffers to meet the time requirements for data processing to ensure the real-time performance of the system. Through the V2X unit, allocate the ID of the current node, and add the new node to the experimental self-organizing network to ensure that the new node can perform subsequent data exchange and communicate with other networked nodes. During the initialization process, the new node checks the status of the hardware interface and completes the function check of the sensors to ensure the normal operation of the device.

[0032] Step 2: Time synchronization. Obtain the synchronization signal and the standard global time through the time synchronization and compensation module. Determine whether to perform time synchronization by judging whether the synchronization signal P is triggered. If triggered, perform clock correction of the local time of the new node through the clock model and filtering algorithm constructed by the time synchronization and compensation module and the node controller to synchronize the local time to the global standard UTC time axis to achieve high-precision time synchronization; if not triggered, proceed to the next step.

[0033] Specifically, the controller determines whether it has received the standard second pulse signal P (i.e., the time synchronization signal) and the best position information sent by the GNSS receiver. When the controller receives the signal, it immediately triggers an interrupt and enters the interrupt program, otherwise it jumps to the next step.

[0034] Specifically, the best position information contains GPS time and longitude and latitude information. Among them, the GPS time can be converted into the world coordinated time (UTC) time used daily. The second pulse signal P is stably generated at a certain frequency interval and for ordinary GNSS receivers, its error does not exceed 20 ns.

[0035] Specifically, immediately query the local time maintained by the controller at this moment in the interrupt program , where represents the real time corresponding to the description; because in actual experiments, the description and recording of time by units such as the controller are discrete, so Expressed in discrete form as ; Because in actual work, there is a decoding delay when the controller decodes the information sent by the receiver , that is, not in local time The corresponding global standard time is directly calculated at the moment , the controller should be time to solve, if we use To update synchronously It will be very complicated, so the synchronization signal and the standard time of the last synchronization signal are used. to synchronize.

[0036] Determine whether the signal is continuous with the previous one. Specifically, use the local time read in the previous step. The local time saved when the synchronization signal was last read To make a difference, that is: ;

[0037] in It is set to 0 by default during initialization, indicating the local time when the last synchronization signal P was triggered. is the local time when the synchronization signal P is triggered. If the difference between its time difference and the fixed time interval is within the error range, as shown in the following formula; ;

[0038] in is the predefined tolerance error range, The second pulse interval indicated above is 1 second, which is set according to the system accuracy. If this formula is satisfied, the signal is considered continuous and the local time is updated, as shown in the following formula; otherwise, the local time is not updated. ;

[0039] in It is the local time maintained by the node controller in real time. It is the interrupt delay of the node controller, which is a constant value set according to the hardware characteristics and interrupt mechanism of the controller. This method can be used to synchronize the local time with the GPS time every second.

[0040] Then read and decode the best position information to obtain the standard global time ,renew and ; ; ;

[0041] Furthermore, for local time , if the minimum time accuracy is set to 0.1 ms, it is impossible to achieve synchronization with an accuracy of 0.1 ms relying on the second pulse signal. Errors will still accumulate within one second. In practice, units such as controllers use crystal oscillators to provide time. The frequency error of ordinary crystal oscillators is generally dozens of PPM (parts per million), that is, there are dozens of microseconds of error per second. Assuming it is seconds. Then the time within each second can be aligned according to the coefficient using the following formula: ;

[0042] Step 3: Data acquisition. The data of GNSS, IMU, and V2X sensors are obtained in real time through the data acquisition and processing module. GNSS provides high-precision global positioning information, IMU obtains the acceleration and angular velocity information of the node, and V2X obtains the data required for cooperative positioning from surrounding nodes, including relative distance, speed, and other state parameters.

[0043] Specifically, in practice, the frequencies of IMU, V2X, and GNSS are arranged from high to low. Therefore, first, the position is estimated by continuously outputting data from the IMU. Let the node state be where is the target position, is the speed, is the attitude. The position and speed updates provided by the IMU are described by the following inertial navigation equations: ; ;

[0044] where the subscripts , are time series, is the acceleration provided by the IMU, is the IMU data time interval. The attitude is updated using the following formula: ;

[0045] where indicates that quaternion multiplication is an algorithm, is the incremental attitude obtained by integrating the angular velocity, is the angular velocity output by the IMU.

[0046] Then, the predicted target state based on the IMU output is expressed using the prediction step of Kalman as: ; ;

[0047] is the predicted state variable, is the state transition matrix (determined by the IMU dynamic equation), is the control input (such as acceleration and angular velocity), is the predicted state covariance matrix, is the process noise covariance matrix.

[0048] More specifically, ; ;

[0049] where is the identity matrix, used to describe the autocorrelation of the state, and the meanings of other parameters are the same as those described above.

[0050] When the positioning information of V2X or the positioning information of GNSS arrives, the update step of the Kalman filter is performed. First, for the observation model is defined as: ;

[0051] where is the observation matrix, is the measurement noise, and the meanings of other parameters are the same as those described above. Then the update step is expressed as: ; ; ;

[0052] where represents the filtering gain, is the observation noise, and correspond to the state and covariance at time k respectively. The superscript represents the prediction at the corresponding time. For example, represents the state prediction at time k, and the meanings of other parameters are the same as those described above. Thus, the target state update is obtained.

[0053] Specifically, the positioning data and the previously received raw data are first stored in the corresponding buffer. When the amount of data in the buffer reaches the set value, the data of the corresponding size is stored in the memory card at one time for preservation.

[0054] Specifically, it is judged whether to export the data. If so, the next step is performed; otherwise, it returns to step two.

[0055] Step four: According to the conditions, select the WIFI or wired connection method to export the data stored in the memory card to the upper computer.

[0056] Further, first load the data of the RTK (Real-Time Kinematic) base station. Secondly, perform differential processing on the exported raw data and the base station data on the calculation software to obtain a more accurate positioning value, usually at the millimeter level, which is used as the reference true value. Then analyze and compare the data, generate visual data and record the experimental results, thus completing a complete set of experiments and data analysis on Internet of Things collaborative positioning.

[0057] Example 2: Refer to Figure 5 , the software module composition of an Internet of Things collaborative positioning experimental system for college teaching provided by the second embodiment of the present invention includes: a self-organizing network communication module, a data acquisition module, a time synchronization and compensation module, a data processing and calculation module, a data recording module, an interface configuration and management module, and a network connection control module; the data processing and calculation module is respectively connected to the data acquisition module, the time synchronization and compensation module, the self-organizing network module, and the data recording module through the interface configuration and management module and the network connection control module, and is used for positioning, time synchronization and compensation, self-organizing network communication, and data recording of the collected data.

[0058] Among them: the interface configuration and management module is used to configure the sensor interface and the function-expandable college teaching use platform, including setting the communication interface parameters configuration and related parameter settings of the self-organizing network communication module, the communication channel settings and rate and other parameter settings of the data acquisition module and the data recording module, and the interface configuration of the network connection module; The self-organizing network communication module supports nodes to obtain IDs from self-organizing network terminals through a wireless communication protocol and add them to the self-organizing network, including obtaining and generating unique IDs within the network, realizing communication and data interaction with other nodes, supporting the dynamic joining and leaving of multiple nodes, and having high flexibility and stability, and is suitable for multi-node collaborative positioning experiments in college teaching environments.

[0059] The data acquisition module is used to collect data from sensors such as V2X, IMU, and GNSS. Use a high-precision GNSS receiver to collect satellite positioning information, the IMU to obtain acceleration and angular velocity data, and the V2X module to obtain relative position information, speed information, and other parameters required for collaborative positioning from adjacent nodes; support multiple data communication protocols (such as UART, SPI, I2C), have scalability, and can connect additional sensors according to experimental requirements.

[0060] The time synchronization and compensation module performs high-precision time synchronization and compensation on the collected data to ensure that the timestamps of all node and sensor data are consistent. Adopt a time synchronization mechanism based on synchronization signals, and combine a time compensation algorithm to finally unify the data to the global UTC time axis, solve the problem of multi-source sensor time axis alignment, and improve the collaborative positioning accuracy; The data processing and calculation module performs fusion processing and positioning calculation on the sensor data after time synchronization. Based on algorithms such as Kalman filtering, it performs collaborative positioning calculation on GNSS, IMU, and V2X data, calculates the real-time position and motion state of the node, and achieves high-precision collaborative positioning.

[0061] The data recording module locally stores the positioning results, original sensor data, and experimental logs generated during the experiment. It uses an embedded storage device (such as an SD card or an eMMC storage chip) to save the data. At the same time, it supports batch exporting the stored data to the host computer or cloud platform for subsequent analysis and verification of the experimental results, and can support the accumulation and traceability of long-term experimental data. The network connection control module is responsible for connecting the experimental system to the external network, supporting remote data transmission and remote monitoring of the experimental process. Through this module, the experimental data can be uploaded to the cloud platform, facilitating teachers and students to access the experimental results anytime and anywhere. It supports communication with a remote server to implement system parameter adjustment and firmware update functions.

[0062] It should be noted that in the above-mentioned multiple embodiments, the included modules are only divided according to functional logic and are not limited to the above division as long as the corresponding functions can be achieved. In addition, the specific names of the functional units are only for easy distinction and do not limit the protection scope of the present invention.

[0063] Finally, the above is only the preferred embodiment of the present invention and the applied technical principles. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described here. Various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. An Internet of Things collaborative positioning experimental system for college teaching, characterized in that, It includes a hardware device and software modules. The hardware device includes a GNSS receiver unit (1), an IMU unit (2), a V2X unit (3), a host computer (4), a WIFI unit (5), a memory card (6) and a controller. The GNSS receiver unit (1), the IMU unit (2), the V2X unit (3), the WIFI module (5), and the memory card (6) are all connected to the controller. The host computer (4) is connected to the controller through a serial port or the WIFI unit (5). The software modules include an ad-hoc network communication module, a data acquisition module, a time synchronization and compensation module, a data processing and calculation module, a data recording module, an interface configuration and management module, and a networked control module. The data processing and calculation module is connected to the data acquisition module, the time synchronization and compensation module, the ad-hoc network module, and the data recording module through the interface configuration and management module and the networked control module respectively, and is used for positioning, time synchronization and compensation, ad-hoc network communication, and data recording of the collected data.

2. The IoT collaborative positioning experimental system for college teaching according to claim 1, wherein the GNSS receiver unit (1) is used to receive satellite positioning signals, GPS time, and second pulse signals for time synchronization; the IMU unit (2) is used to obtain its own motion state and attitude information, and jointly with the GNSS receiver unit (1) and the V2X unit (3) information to achieve collaborative positioning; the V2X unit (3) is used to obtain roadside node and surrounding moving node information, and realize local area networking with surrounding nodes, obtain the relative distance from surrounding nodes, and achieve collaborative positioning; the host computer (4) is used to analyze the data stored in the IoT collaborative positioning nodes, calculate the positioning coordinate trajectory that can be used as the experimental true value through software, and conduct experimental analysis; the WIFI unit (5) is used to interact with the collaborative positioning nodes through a wireless network to achieve the upload of relevant data or function configuration; the memory card (6) is used to save positioning data, sensor raw data, and relevant log information; the controller is used to receive and process the measurement data of the GNSS receiver unit (1), the IMU unit (2), and the V2X unit (3).

3. The IoT collaborative positioning experimental system for college teaching according to claim 1, wherein the ad-hoc network communication module is used to support nodes to obtain IDs from ad-hoc network terminals and add them to the ad-hoc network; the data acquisition module is used to collect data from the V2X unit, the IMU unit, and the GNSS receiver unit; the time synchronization and compensation module is used for the synchronization of the local time of multiple nodes with the global standard time to achieve the unification of distributed nodes; the data processing and calculation module is used for data processing to achieve collaborative positioning calculation; the interface configuration and management module is used to configure the sensor interface and the function-expandable college teaching use platform; the data recording module is used for data storage and operation log preservation; The networked control module is used to communicate with a remote server or cloud platform via wired or wireless means to achieve data transmission and remote monitoring.

4. An Internet of Things collaborative positioning experimental system for college teaching as described in claim 1, characterized in that, The interface configuration and management module adopts configurable software and hardware interfaces, supports multiple communication protocols such as SPI, I2C, UART, CAN, and USB, uploads the result data and original data to the PC side, supports the batch export and analysis functions of experimental data, and performs trend analysis, error evaluation, and model verification on the exported experimental data through built-in analysis tools, facilitating the evaluation and improvement of teaching experiments.

5. The Internet of Things collaborative positioning experimental system for college teaching according to claim 1, characterized in that, The networked control module supports the remote configuration management function, allowing the adjustment of system parameters and algorithm configurations through a remote server to adapt to different experimental requirements and environmental conditions.

6. An Internet of Things collaborative positioning experimental method for college teaching, characterized by comprising the following steps: S1. Initialize a new node, configure the sensor interface, and configure the node ID through the self-organizing network communication module to achieve the local area networking of the new node; wherein, the node refers to the positioning device terminal in the network, and the new node refers to the new device terminal joining the network. S2. The initialized new node obtains the synchronization signal P and the standard global time through the time synchronization and compensation module, judges whether to perform time synchronization according to whether the synchronization signal P is triggered. If triggered, the local time of the new node is corrected by constructing a clock model to synchronize the local time to the global standard time axis to achieve high-precision time synchronization; if not triggered, proceed to the next step. S3. Obtain the data of GNSS, IMU, and V2X through the data acquisition and processing module, synchronize the data with different timestamps of different sensors to the same time through the time synchronization and compensation module, achieve Internet of Things-based collaborative positioning through the fusion positioning algorithm, and locally store the results and original data. Judge whether data needs to be exported. If so, proceed to the next step; otherwise, return to S2 and loop. S4. Export the experimental results and original data to the upper computer through the relevant interface, obtain the experimental reference true value through the differential processing of the original data and the reference value, perform result data analysis and comparison in the experimental session, generate visual data and record the experimental results, thereby completing a complete set of Internet of Things collaborative positioning experiments and data analysis.

7. The Internet of Things collaborative positioning experiment method for college teaching according to claim 6, characterized in that, The specific content of step S2 includes: S21. The time synchronization and compensation module sends a synchronization signal P to the node controller via a signal line at fixed time intervals and simultaneously sends the current global standard time to the node controller via a data line ; S22. After the node controller receives the synchronization signal P, it triggers an external interrupt and obtains the local time at the current moment within the interrupt program. ; S23. Determine whether it is continuous with the previous signal, using the local time read in the previous step and the local time saved when the synchronization signal was read last time to calculate the difference, that is: ； Among them is default set to 0 during initialization, representing the local time when the previous synchronization signal P was triggered is the local time when the current synchronization signal P is triggered. If the difference between its time difference and a fixed time interval is within the error range, the signal is considered continuous and proceeds to the next step; as shown in the following formula: ； wherein is a predefined tolerance error range, representing a fixed time interval of the synchronization signal P; If the conditions are not met, it is considered that the previous signal was lost or this signal is the moment when the synchronization signal P is first captured after initialization. For both of these cases, skip S24 and directly proceed to S25. S24. According to the judgment of S23, if the synchronization signal is continuous, perform the time synchronization step according to the formula: ； wherein is the local time maintained by the node controller in real time, is the interrupt latency of the node controller, which is set according to the hardware characteristics and interrupt mechanism of the node controller; then proceed to the next step, read and decode to obtain ; S25. Read and decode the time at the moment of sending the synchronization signal P, and update , and ; ； ； Thus, the node time synchronization is completed.

8. The Internet of Things collaborative positioning experiment method for college teaching according to claim 7, characterized in that, The specific content of step S3 includes: S31. Data acquisition, real-time obtain the data of GNSS, IMU, and V2X sensors through the data acquisition and processing module; GNSS provides high-precision global positioning information, IMU obtains the acceleration and angular velocity information of the node, and V2X obtains the data required for collaborative positioning from surrounding nodes, including relative distance, speed, and other state parameters. S32. Unify the data of the above different sensors to the same standard time axis through the time synchronization and compensation module; utilize S24 and S25 to complete time synchronization, realize the compensation and alignment of the acquisition time of sensor data, and ensure the timing consistency of the input data of the fusion algorithm; S33. Judge whether the data is continuous and whether there are packet losses according to the time stamps, perform anomaly detection on the data, if it is normal, proceed to the next step, otherwise re-acquire; S34. Based on the multi-source data after time synchronization, adopt a fusion algorithm for positioning calculation to obtain the real-time position and motion state of the node, where the fusion algorithm includes the Kalman filter algorithm and the particle filter algorithm; S35. Locally store the final positioning result obtained in S34 and the original sensor data for subsequent analysis and verification; S36. Judge whether the data is exported from the local storage to the upper computer or the cloud. If so, jump to step S4 for export, otherwise jump back to the first step S2 for loop.

9. The Internet of Things collaborative positioning experiment method for college teaching according to claim 7, characterized in that, The specific steps of step S34 include: Since the frequencies of IMU, V2X, and GNSS are arranged in descending order, first continuously estimate the position through the data output by the IMU, and update the Kalman filter algorithm when the positioning information of V2X or GNSS arrives, so as to obtain the real-time position and motion state of the node.

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